Observational Study of the Efficiency of Treatments in Patients Hospitalized with Covid-19 in Madrid
Nikolas Bernaola, Raquel Mena, Ander Bernaola, Cesar Carballo, Antonio Lara, Concha Bielza, Pedro Larrañaga
doi:10.1101/2020.07.17.20155960
Background Many different treatments were heavily administered to patients with COVID-19 during the peak of the pandemic in Madrid without robust evidence supporting them.
Methods We examined the association between sixteen treatments in four groups (steroids, antivirals, antibiotics and immunomodulators) and intubation or death. Data were obtained from patients that were admitted to an HM hospital with suspicion of COVID-19 until 24/04/2020, excluding unconfirmed diagnosis, those who were admitted before the epidemic started in Madrid, had an outcome that was not discharge or death or died within 24 hours of presentation. We compared outcomes between treated and untreated patients using propensity-score caliper matching.
Results Of 2,307 patients in the dataset, 679 were excluded. Of the remaining 1,645 patients, 263 (16%) died and 311 (18.9%) died or were intubated. Except for hydroxychloroquine and prednisone, patients that were treated with any of the medications were more likely to go through an outcome of death or intubation at baseline. After propensity matching we found an association between treatment with hydroxychloroquine and prednisone and better outcomes (hazard ratios with 95% CI of 0.83 ± 0.06 and 0.85 ± 0.03). Results were similar in multiple sensitivity analyses.
Conclusions In this multicenter study of patients admitted with COVID-19 hydroxychloroquine and prednisone administration was found to be associated with improved outcomes. Other treatments were associated with no effect or worse outcomes. Randomized, controlled trials of these medications in patients with COVID-19 are needed to avoid heavy administration of treatments with no strong evidence to support them. .
Appendix 1: HM dataset description The following text has been copied from a document released by the HM hospital network along with its database explaining its project and the structure of the dataset. The information is organized in tables according to their content, all of them linked by a unique admission identifier. This identifier is the de-anonymization key, explicitly created for this purpose, and has nothing to do with the actual identifier of each admission.
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